中国水资源压力的时空变化及其驱动因素:多尺度分析

IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Ecological Indicators Pub Date : 2024-11-14 DOI:10.1016/j.ecolind.2024.112820
Jianmin Qiao , Qin Zhang , Jing Shao , Qian Cao , Haimeng Liu , Furong Lv
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引用次数: 0

摘要

水资源是维持自然生态系统和人类活动的基础,在区域环境的可持续发展中发挥着至关重要的作用。然而,在人类活动和气候变化的双重压力下,水资源的压力日益明显,已成为未来十年全球最大的风险之一。在本研究中,我们运用水资源压力指数、洛伦兹曲线和泰尔指数,探讨了流域和盆地两个尺度上水资源压力的时空模式和不平等分布特征。此外,我们还利用偏最小二乘法回归找出了影响水资源压力的关键因素。结果表明,2002 年至 2020 年期间,中国水资源压力存在明显的区域差异。在流域尺度上,水资源紧张指数大于 0.4 的地区主要分布在东部、东北部和西北部地区。而在流域尺度上,则出现了南低北高的格局。海河和淮河流域的水压力最大。洛伦兹曲线明显偏离绝对平等线,表明区域水资源压力的不平等程度较高。Theil 指数从 1.26 上升到 1.50,表明不平等程度略有上升趋势。对驱动因素的分析表明,黄河流域主要受 GDP 和人口的影响,松花江流域受人口和城市土地利用的影响,西南河流域主要受植被覆盖的驱动。总体而言,降水是影响水资源压力的最关键驱动因素,主要产生负面影响。该研究为缓解区域水资源压力提供了理论依据,并为水资源的科学规划和管理提供了宝贵的启示。
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Spatial and temporal variation of water stress in China and its driving factors: A multi-scale analysis
Water resources are fundamental for sustaining natural ecosystems and human activities, playing a critical role in the sustainable development of the regional environment. Under the dual pressures of human activities and climate change, however, the stress on water resources has become increasingly evident, emerging as one of the greatest global risks for the next decade. In this study, by applying the water stress index, Lorenz curve, and Theil index, we explored the spatiotemporal patterns and inequality distribution characteristics of water resource stress across two scales: catchment and basin. Additionally, we used partial least squares regression to identify the key factors influencing water resource stress. The results indicated significant regional variations in water stress across China during 2002 to 2020. At the catchment scale, areas with a water stress index greater than 0.4 were distributed in the eastern, northeastern and northwestern regions. While at the basin scale, a north–south pattern emerged with lower stress in the south and higher stress in the north. The Haihe and Huaihe river basins exhibited the highest water stress. The Lorenz curve deviated significantly from the line of absolute equality, indicating a high degree of inequality in regional water resource stress. The Theil index increased from 1.26 to 1.50, showing a slight upward trend in inequality. Analysis of the driving factors revealed that the Yellow River Basin was primarily influenced by GDP and population, the Songhua River Basin was affected by population and urban land use, and the Southwest River Basin is driven mainly by vegetation cover. Overall, precipitation was the most critical driver affecting water stress, predominantly exerting a negative influence. This study provides a theoretical basis for alleviating regional water stress and offers valuable insights for the scientific planning and management of water resources.
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来源期刊
Ecological Indicators
Ecological Indicators 环境科学-环境科学
CiteScore
11.80
自引率
8.70%
发文量
1163
审稿时长
78 days
期刊介绍: The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.
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